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A Hybrid Rule-Based and Machine Learning System for Arabic Check Courtesy Amount Recognition.
Irfan Ahmad1,2
1Information and Computer Science Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
Sensors (Basel, Switzerland)
|May 13, 2023
Summary
This study introduces a novel hybrid system for recognizing courtesy amounts on Arabic bank checks using Indian digits. The system achieves 67.4% accuracy at the amount level, establishing a new benchmark for this task.
Area of Science:
- Pattern Recognition
- Machine Learning
- Computer Vision
Background:
- Courtesy amount recognition from bank checks is crucial.
- Existing research lacks focus on Arabic checks with Indian digits.
- Arabic check recognition presents unique challenges.
Purpose of the Study:
- To develop an end-to-end system for Arabic check courtesy amount recognition.
- To investigate and compare segmentation-based and segmentation-free approaches.
- To establish a baseline for future research in this domain.
Main Methods:
- Developed a hybrid system combining rule-based and machine learning modules.
- Implemented both segmentation-based and segmentation-free recognition strategies.
- Evaluated the system on the CENPARMI dataset of Arabic bank checks.
Main Results:
- Achieved 67.4% accuracy at the amount level.
- Achieved 87.15% accuracy at the digit level.
- Demonstrated the system's effectiveness on a dataset of 626 check images.
Conclusions:
- The developed hybrid system provides a robust solution for Arabic check courtesy amount recognition.
- The system's performance sets a new benchmark for the field.
- Identified areas for future research to further improve accuracy and applicability.
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